




"""simple docstring"""







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE_	     =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class 				snake_case_			(					_UpperCAmelCase			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A_				      =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
   A_				      =       DPRContextEncoderTokenizer

class 				snake_case_			(					_UpperCAmelCase			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A_				      =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
   A_				      =       DPRQuestionEncoderTokenizer
SCREAMING_SNAKE_CASE_	     =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

SCREAMING_SNAKE_CASE_	     =       collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


SCREAMING_SNAKE_CASE_	     =       R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(_UpperCAmelCase			)
class 				snake_case_			:







   """simple docstring"""




   def __call__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   BatchEncoding:
       if titles is None and texts is None:
           return super().__call__(
               lowerCamelCase_       ,					padding=lowerCamelCase_       ,					truncation=lowerCamelCase_       ,					max_length=lowerCamelCase_       ,					return_tensors=lowerCamelCase_       ,					return_attention_mask=lowerCamelCase_       ,					**lowerCamelCase_       ,					)
       elif titles is None or texts is None:
           UpperCamelCase              =	titles if texts is None else texts
           return super().__call__(
               lowerCamelCase_       ,					lowerCamelCase_       ,					padding=lowerCamelCase_       ,					truncation=lowerCamelCase_       ,					max_length=lowerCamelCase_       ,					return_tensors=lowerCamelCase_       ,					return_attention_mask=lowerCamelCase_       ,					**lowerCamelCase_       ,					)
       UpperCamelCase              =	titles if not isinstance(lowerCamelCase_       ,					lowerCamelCase_) else [titles]
       UpperCamelCase              =	texts if not isinstance(lowerCamelCase_       ,					lowerCamelCase_) else [texts]
       UpperCamelCase              =	len(lowerCamelCase_)
       UpperCamelCase              =	questions if not isinstance(lowerCamelCase_       ,					lowerCamelCase_) else [questions] * n_passages
       assert len(lowerCamelCase_) == len(
           lowerCamelCase_), F'There should be as many titles than texts but got {len(lowerCamelCase_)} titles and {len(lowerCamelCase_)} texts.'
       UpperCamelCase              =	super().__call__(lowerCamelCase_       ,					lowerCamelCase_       ,					padding=lowerCamelCase_       ,					truncation=lowerCamelCase_)['''input_ids''']
       UpperCamelCase              =	super().__call__(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_       ,					padding=lowerCamelCase_       ,					truncation=lowerCamelCase_)['''input_ids''']
       UpperCamelCase              =	{
           '''input_ids''': [
               (encoded_question_and_title + encoded_text)[:max_length]
               if max_length is not None and truncation
               else encoded_question_and_title + encoded_text
               for encoded_question_and_title, encoded_text in zip(lowerCamelCase_       ,					lowerCamelCase_)
           ]
       }
       if return_attention_mask is not False:
           UpperCamelCase              =	[]
           for input_ids in encoded_inputs["input_ids"]:
               attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
           UpperCamelCase              =	attention_mask
       return self.pad(lowerCamelCase_       ,					padding=lowerCamelCase_       ,					max_length=lowerCamelCase_       ,					return_tensors=lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = 1_6       ,					lowerCamelCase_ = 6_4       ,					lowerCamelCase_ = 4       ,					)				->   List[DPRSpanPrediction]:
       UpperCamelCase              =	reader_input['''input_ids''']
       UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	reader_output[:3]
       UpperCamelCase              =	len(lowerCamelCase_)
       UpperCamelCase              =	sorted(range(lowerCamelCase_)       ,					reverse=lowerCamelCase_       ,					key=relevance_logits.__getitem__)
       UpperCamelCase              =	[]
       for doc_id in sorted_docs:
           UpperCamelCase              =	list(input_ids[doc_id])
           # assuming question & title information is at the beginning of the sequence
           UpperCamelCase              =	sequence_ids.index(self.sep_token_id       ,					2) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
               UpperCamelCase              =	sequence_ids.index(self.pad_token_id)
           else:
               UpperCamelCase              =	len(lowerCamelCase_)

           UpperCamelCase              =	self._get_best_spans(
               start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,					end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,					max_answer_length=lowerCamelCase_       ,					top_spans=lowerCamelCase_       ,					)
           for start_index, end_index in best_spans:
               start_index += passage_offset
               end_index += passage_offset
               nbest_spans_predictions.append(
                   DPRSpanPrediction(
                       span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,					relevance_score=relevance_logits[doc_id]       ,					doc_id=lowerCamelCase_       ,					start_index=lowerCamelCase_       ,					end_index=lowerCamelCase_       ,					text=self.decode(sequence_ids[start_index : end_index + 1])       ,					))
           if len(lowerCamelCase_) >= num_spans:
               break
       return nbest_spans_predictions[:num_spans]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   List[DPRSpanPrediction]:
       UpperCamelCase              =	[]
       for start_index, start_score in enumerate(lowerCamelCase_):
           for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
               scores.append(((start_index, start_index + answer_length), start_score + end_score))
       UpperCamelCase              =	sorted(lowerCamelCase_       ,					key=lambda lowerCamelCase_: x[1]       ,					reverse=lowerCamelCase_)
       UpperCamelCase              =	[]
       for (start_index, end_index), score in scores:
           assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
           UpperCamelCase              =	end_index - start_index + 1
           assert length <= max_answer_length, F'Span is too long: {length} > {max_answer_length}'
           if any(
               start_index <= prev_start_index <= prev_end_index <= end_index
               or prev_start_index <= start_index <= end_index <= prev_end_index
               for (prev_start_index, prev_end_index) in chosen_span_intervals):
               continue
           chosen_span_intervals.append((start_index, end_index))

           if len(lowerCamelCase_) == top_spans:
               break
       return chosen_span_intervals

@add_end_docstrings(_UpperCAmelCase			)
class 				snake_case_			(					_UpperCAmelCase	, _UpperCAmelCase			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       READER_PRETRAINED_VOCAB_FILES_MAP
   A_				      =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       READER_PRETRAINED_INIT_CONFIGURATION
   A_				      =       ['''input_ids''', '''attention_mask''']
   A_				      =       DPRReaderTokenizer






"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






